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model_mine.py
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model_mine.py
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import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
from resnet import resnet50, resnet18
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.div(norm)
return out
class Non_local(nn.Module):
def __init__(self, in_channels, reduc_ratio=2):
super(Non_local, self).__init__()
self.in_channels = in_channels
self.inter_channels = reduc_ratio//reduc_ratio
self.g = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1,
padding=0),
)
self.W = nn.Sequential(
nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(self.in_channels),
)
nn.init.constant_(self.W[1].weight, 0.0)
nn.init.constant_(self.W[1].bias, 0.0)
self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
self.phi = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
kernel_size=1, stride=1, padding=0)
def forward(self, x):
'''
:param x: (b, c, t, h, w)
:return:
'''
batch_size = x.size(0)
g_x = self.g(x).view(batch_size, self.inter_channels, -1)
g_x = g_x.permute(0, 2, 1)
theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
theta_x = theta_x.permute(0, 2, 1)
phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
f = torch.matmul(theta_x, phi_x)
N = f.size(-1)
# f_div_C = torch.nn.functional.softmax(f, dim=-1)
f_div_C = f / N
y = torch.matmul(f_div_C, g_x)
y = y.permute(0, 2, 1).contiguous()
y = y.view(batch_size, self.inter_channels, *x.size()[2:])
W_y = self.W(y)
z = W_y + x
return z
# #####################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_out')
init.zeros_(m.bias.data)
elif classname.find('BatchNorm1d') != -1:
init.normal_(m.weight.data, 1.0, 0.01)
init.zeros_(m.bias.data)
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.normal_(m.weight.data, 0, 0.001)
if m.bias:
init.zeros_(m.bias.data)
class visible_module(nn.Module):
def __init__(self, arch='resnet50', share_net=1):
super(visible_module, self).__init__()
model_v = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
self.share_net = share_net
if self.share_net == 0:
pass
else:
self.visible = nn.ModuleList()
self.visible.conv1 = model_v.conv1
self.visible.bn1 = model_v.bn1
self.visible.relu = model_v.relu
self.visible.maxpool = model_v.maxpool
if self.share_net > 1:
for i in range(1, self.share_net):
setattr(self.visible,'layer'+str(i), getattr(model_v,'layer'+str(i)))
def forward(self, x):
if self.share_net == 0:
return x
else:
x = self.visible.conv1(x)
x = self.visible.bn1(x)
x = self.visible.relu(x)
x = self.visible.maxpool(x)
if self.share_net > 1:
for i in range(1, self.share_net):
x = getattr(self.visible, 'layer'+str(i))(x)
return x
class thermal_module(nn.Module):
def __init__(self, arch='resnet50', share_net=1):
super(thermal_module, self).__init__()
model_t = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
self.share_net = share_net
if self.share_net == 0:
pass
else:
self.thermal = nn.ModuleList()
self.thermal.conv1 = model_t.conv1
self.thermal.bn1 = model_t.bn1
self.thermal.relu = model_t.relu
self.thermal.maxpool = model_t.maxpool
if self.share_net > 1:
for i in range(1, self.share_net):
setattr(self.thermal,'layer'+str(i), getattr(model_t,'layer'+str(i)))
def forward(self, x):
if self.share_net == 0:
return x
else:
x = self.thermal.conv1(x)
x = self.thermal.bn1(x)
x = self.thermal.relu(x)
x = self.thermal.maxpool(x)
if self.share_net > 1:
for i in range(1, self.share_net):
x = getattr(self.thermal, 'layer'+str(i))(x)
return x
class base_resnet(nn.Module):
def __init__(self, arch='resnet50', share_net=1):
super(base_resnet, self).__init__()
model_base = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
model_base.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.share_net = share_net
if self.share_net == 0:
self.base = model_base
else:
self.base = nn.ModuleList()
if self.share_net > 4:
pass
else:
for i in range(self.share_net, 5):
setattr(self.base,'layer'+str(i), getattr(model_base,'layer'+str(i)))
def forward(self, x):
if self.share_net == 0:
x = self.base.conv1(x)
x = self.base.bn1(x)
x = self.base.relu(x)
x = self.base.maxpool(x)
x = self.base.layer1(x)
x = self.base.layer2(x)
x = self.base.layer3(x)
x = self.base.layer4(x)
return x
elif self.share_net > 4:
return x
else:
for i in range(self.share_net, 5):
x = getattr(self.base, 'layer'+str(i))(x)
return x
class embed_net(nn.Module):
def __init__(self, class_num, no_local= 'off', gm_pool = 'on', arch='resnet50', share_net=1, pcb='on',local_feat_dim=256, num_strips=6):
super(embed_net, self).__init__()
self.thermal_module = thermal_module(arch=arch, share_net=share_net)
self.visible_module = visible_module(arch=arch, share_net=share_net)
self.base_resnet = base_resnet(arch=arch, share_net=share_net)
self.non_local = no_local
self.pcb = pcb
if self.non_local =='on':
pass
pool_dim = 2048
self.l2norm = Normalize(2)
self.gm_pool = gm_pool
if self.pcb == 'on':
self.num_stripes=num_strips
local_conv_out_channels=local_feat_dim
self.local_conv_list = nn.ModuleList()
for _ in range(self.num_stripes):
conv = nn.Conv2d(pool_dim, local_conv_out_channels, 1)
conv.apply(weights_init_kaiming)
self.local_conv_list.append(nn.Sequential(
conv,
nn.BatchNorm2d(local_conv_out_channels),
nn.ReLU(inplace=True)
))
self.fc_list = nn.ModuleList()
for _ in range(self.num_stripes):
fc = nn.Linear(local_conv_out_channels, class_num)
init.normal_(fc.weight, std=0.001)
init.constant_(fc.bias, 0)
self.fc_list.append(fc)
else:
self.bottleneck = nn.BatchNorm1d(pool_dim)
self.bottleneck.bias.requires_grad_(False) # no shift
self.classifier = nn.Linear(pool_dim, class_num, bias=False)
self.bottleneck.apply(weights_init_kaiming)
self.classifier.apply(weights_init_classifier)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
def forward(self, x1, x2, modal=0):
if modal == 0:
x1 = self.visible_module(x1)
x2 = self.thermal_module(x2)
x = torch.cat((x1, x2), 0)
elif modal == 1:
x = self.visible_module(x1)
elif modal == 2:
x = self.thermal_module(x2)
# shared block
if self.non_local == 'on':
pass
else:
x = self.base_resnet(x)
if self.pcb == 'on':
feat = x
assert feat.size(2) % self.num_stripes == 0
stripe_h = int(feat.size(2) / self.num_stripes)
local_feat_list = []
logits_list = []
for i in range(self.num_stripes):
# shape [N, C, 1, 1]
# average pool
#local_feat = F.avg_pool2d(feat[:, :, i * stripe_h: (i + 1) * stripe_h, :],(stripe_h, feat.size(-1)))
if self.gm_pool == 'on':
# gm pool
local_feat = feat[:, :, i * stripe_h: (i + 1) * stripe_h, :]
b, c, h, w = local_feat.shape
local_feat = local_feat.view(b,c,-1)
p = 10.0 # regDB: 10.0 SYSU: 3.0
local_feat = (torch.mean(local_feat**p, dim=-1) + 1e-12)**(1/p)
else:
# average pool
#local_feat = F.avg_pool2d(feat[:, :, i * stripe_h: (i + 1) * stripe_h, :],(stripe_h, feat.size(-1)))
local_feat = F.max_pool2d(feat[:, :, i * stripe_h: (i + 1) * stripe_h, :],(stripe_h, feat.size(-1)))
# shape [N, c, 1, 1]
local_feat = self.local_conv_list[i](local_feat.view(feat.size(0),feat.size(1),1,1))
# shape [N, c]
local_feat = local_feat.view(local_feat.size(0), -1)
local_feat_list.append(local_feat)
if hasattr(self, 'fc_list'):
logits_list.append(self.fc_list[i](local_feat))
feat_all = [lf for lf in local_feat_list]
feat_all = torch.cat(feat_all, dim=1)
if self.training:
return local_feat_list, logits_list, feat_all
else:
return self.l2norm(feat_all)
else:
if self.gm_pool == 'on':
b, c, h, w = x.shape
x = x.view(b, c, -1)
p = 3.0
x_pool = (torch.mean(x**p, dim=-1) + 1e-12)**(1/p)
else:
x_pool = self.avgpool(x)
x_pool = x_pool.view(x_pool.size(0), x_pool.size(1))
feat = self.bottleneck(x_pool)
if self.training:
return x_pool, self.classifier(feat)#, scores
else:
return self.l2norm(x_pool), self.l2norm(feat)